The impact of the National Science Foundation’s Innovation Corps (I-Corps) on academic innovation and entrepreneurship
Bibliographic record
Abstract
Abstract: In 2011, the U.S. National Science Foundation created the Innovation Corps (I-Corps) program in an effort to explore ways to translate the results of the academic research the agency has funded into new products, processes, devices, or services and move them to the marketplace. The agency established a 3-tier structure to support the implementation of the I-Corps concept. Selected I-Corps teams consisting of the principal investigator, an entrepreneurial lead, and an industry mentor participate in a 7-week accelerated version of the Lean Launchpad methodology that was first developed by Steve Blank at Stanford University. Participating teams engage in talking to potential customers, partners, and competitors and address the challenges and the uncertainty of creating successful ventures. I-Corps sites were set up to promote selected aspects of innovation and entrepreneurship ecosystems at the grantee institutions. I-Corps Regional Nodes were charged with recruiting I-Corps teams in a larger geographical area as well as stimulating a new culture of academic entrepreneurship in the institutions in their area of influence. This Topical Review describes the experiences and the impact of the New York City Regional Innovation Node, which is led by the City University of New York, in partnership with New York University and Columbia University.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".